package javacore.draw.gif;

/* NeuQuant Neural-Net Quantization Algorithm  
 * ------------------------------------------  
 *  
 * Copyright (c) 1994 Anthony Dekker  
 *  
 * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.  
 * See "Kohonen neural networks for optimal colour quantization"  
 * in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.  
 * for a discussion of the algorithm.  
 *  
 * Any party obtaining a copy of these files from the author, directly or  
 * indirectly, is granted, free of charge, a full and unrestricted irrevocable,  
 * world-wide, paid up, royalty-free, nonexclusive right and license to deal  
 * in this software and documentation files (the "Software"), including without  
 * limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,  
 * and/or sell copies of the Software, and to permit persons who receive  
 * copies from any such party to do so, with the only requirement being  
 * that this copyright notice remain intact.  
 */   
   
// Ported to Java 12/00 K Weiner   
   
public class NeuQuant {   
   
    protected static final int netsize = 256; /* number of colours used */   
   
    /* four primes near 500 - assume no image has a length so large */   
    /* that it is divisible by all four primes */   
    protected static final int prime1 = 499;   
    protected static final int prime2 = 491;   
    protected static final int prime3 = 487;   
    protected static final int prime4 = 503;   
   
    protected static final int minpicturebytes = (3 * prime4);   
    /* minimum size for input image */   
   
    /* Program Skeleton  
       ----------------  
       [select samplefac in range 1..30]  
       [read image from input file]  
       pic = (unsigned char*) malloc(3*width*height);  
       initnet(pic,3*width*height,samplefac);  
       learn();  
       unbiasnet();  
       [write output image header, using writecolourmap(f)]  
       inxbuild();  
       write output image using inxsearch(b,g,r)      */   
   
    /* Network Definitions  
       ------------------- */   
   
    protected static final int maxnetpos = (netsize - 1);   
    protected static final int netbiasshift = 4; /* bias for colour values */   
    protected static final int ncycles = 100; /* no. of learning cycles */   
   
    /* defs for freq and bias */   
    protected static final int intbiasshift = 16; /* bias for fractions */   
    protected static final int intbias = (((int) 1) << intbiasshift);   
    protected static final int gammashift = 10; /* gamma = 1024 */   
    protected static final int gamma = (((int) 1) << gammashift);   
    protected static final int betashift = 10;   
    protected static final int beta = (intbias >> betashift); /* beta = 1/1024 */   
    protected static final int betagamma =   
        (intbias << (gammashift - betashift));   
   
    /* defs for decreasing radius factor */   
    protected static final int initrad = (netsize >> 3); /* for 256 cols, radius starts */   
    protected static final int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */   
    protected static final int radiusbias = (((int) 1) << radiusbiasshift);   
    protected static final int initradius = (initrad * radiusbias); /* and decreases by a */   
    protected static final int radiusdec = 30; /* factor of 1/30 each cycle */   
   
    /* defs for decreasing alpha factor */   
    protected static final int alphabiasshift = 10; /* alpha starts at 1.0 */   
    protected static final int initalpha = (((int) 1) << alphabiasshift);   
   
    protected int alphadec; /* biased by 10 bits */   
   
    /* radbias and alpharadbias used for radpower calculation */   
    protected static final int radbiasshift = 8;   
    protected static final int radbias = (((int) 1) << radbiasshift);   
    protected static final int alpharadbshift = (alphabiasshift + radbiasshift);   
    protected static final int alpharadbias = (((int) 1) << alpharadbshift);   
   
    /* Types and Global Variables  
    -------------------------- */   
   
    protected byte[] thepicture; /* the input image itself */   
    protected int lengthcount; /* lengthcount = H*W*3 */   
   
    protected int samplefac; /* sampling factor 1..30 */   
   
    //   typedef int pixel[4];                /* BGRc */   
    protected int[][] network; /* the network itself - [netsize][4] */   
   
    protected int[] netindex = new int[256];   
    /* for network lookup - really 256 */   
   
    protected int[] bias = new int[netsize];   
    /* bias and freq arrays for learning */   
    protected int[] freq = new int[netsize];   
    protected int[] radpower = new int[initrad];   
    /* radpower for precomputation */   
   
    /* Initialise network in range (0,0,0) to (255,255,255) and set parameters  
       ----------------------------------------------------------------------- */   
    public NeuQuant(byte[] thepic, int len, int sample) {   
   
        int i;   
        int[] p;   
   
        thepicture = thepic;   
        lengthcount = len;   
        samplefac = sample;   
   
        network = new int[netsize][];   
        for (i = 0; i < netsize; i++) {   
            network[i] = new int[4];   
            p = network[i];   
            p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;   
            freq[i] = intbias / netsize; /* 1/netsize */   
            bias[i] = 0;   
        }   
    }   
       
    public byte[] colorMap() {   
        byte[] map = new byte[3 * netsize];   
        int[] index = new int[netsize];   
        for (int i = 0; i < netsize; i++)   
            index[network[i][3]] = i;   
        int k = 0;   
        for (int i = 0; i < netsize; i++) {   
            int j = index[i];   
            map[k++] = (byte) (network[j][0]);   
            map[k++] = (byte) (network[j][1]);   
            map[k++] = (byte) (network[j][2]);   
        }   
        return map;   
    }   
       
    /* Insertion sort of network and building of netindex[0..255] (to do after unbias)  
       ------------------------------------------------------------------------------- */   
    public void inxbuild() {   
   
        int i, j, smallpos, smallval;   
        int[] p;   
        int[] q;   
        int previouscol, startpos;   
   
        previouscol = 0;   
        startpos = 0;   
        for (i = 0; i < netsize; i++) {   
            p = network[i];   
            smallpos = i;   
            smallval = p[1]; /* index on g */   
            /* find smallest in i..netsize-1 */   
            for (j = i + 1; j < netsize; j++) {   
                q = network[j];   
                if (q[1] < smallval) { /* index on g */   
                    smallpos = j;   
                    smallval = q[1]; /* index on g */   
                }   
            }   
            q = network[smallpos];   
            /* swap p (i) and q (smallpos) entries */   
            if (i != smallpos) {   
                j = q[0];   
                q[0] = p[0];   
                p[0] = j;   
                j = q[1];   
                q[1] = p[1];   
                p[1] = j;   
                j = q[2];   
                q[2] = p[2];   
                p[2] = j;   
                j = q[3];   
                q[3] = p[3];   
                p[3] = j;   
            }   
            /* smallval entry is now in position i */   
            if (smallval != previouscol) {   
                netindex[previouscol] = (startpos + i) >> 1;   
                for (j = previouscol + 1; j < smallval; j++)   
                    netindex[j] = i;   
                previouscol = smallval;   
                startpos = i;   
            }   
        }   
        netindex[previouscol] = (startpos + maxnetpos) >> 1;   
        for (j = previouscol + 1; j < 256; j++)   
            netindex[j] = maxnetpos; /* really 256 */   
    }   
       
    /* Main Learning Loop  
       ------------------ */   
    public void learn() {   
   
        int i, j, b, g, r;   
        int radius, rad, alpha, step, delta, samplepixels;   
        byte[] p;   
        int pix, lim;   
   
        if (lengthcount < minpicturebytes)   
            samplefac = 1;   
        alphadec = 30 + ((samplefac - 1) / 3);   
        p = thepicture;   
        pix = 0;   
        lim = lengthcount;   
        samplepixels = lengthcount / (3 * samplefac);   
        delta = samplepixels / ncycles;   
        alpha = initalpha;   
        radius = initradius;   
   
        rad = radius >> radiusbiasshift;   
        if (rad <= 1)   
            rad = 0;   
        for (i = 0; i < rad; i++)   
            radpower[i] =   
                alpha * (((rad * rad - i * i) * radbias) / (rad * rad));   
   
        //fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);   
   
        if (lengthcount < minpicturebytes)   
            step = 3;   
        else if ((lengthcount % prime1) != 0)   
            step = 3 * prime1;   
        else {   
            if ((lengthcount % prime2) != 0)   
                step = 3 * prime2;   
            else {   
                if ((lengthcount % prime3) != 0)   
                    step = 3 * prime3;   
                else   
                    step = 3 * prime4;   
            }   
        }   
   
        i = 0;   
        while (i < samplepixels) {   
            b = (p[pix + 0] & 0xff) << netbiasshift;   
            g = (p[pix + 1] & 0xff) << netbiasshift;   
            r = (p[pix + 2] & 0xff) << netbiasshift;   
            j = contest(b, g, r);   
   
            altersingle(alpha, j, b, g, r);   
            if (rad != 0)   
                alterneigh(rad, j, b, g, r); /* alter neighbours */   
   
            pix += step;   
            if (pix >= lim)   
                pix -= lengthcount;   
   
            i++;   
            if (delta == 0)   
                delta = 1;   
            if (i % delta == 0) {   
                alpha -= alpha / alphadec;   
                radius -= radius / radiusdec;   
                rad = radius >> radiusbiasshift;   
                if (rad <= 1)   
                    rad = 0;   
                for (j = 0; j < rad; j++)   
                    radpower[j] =   
                        alpha * (((rad * rad - j * j) * radbias) / (rad * rad));   
            }   
        }   
        //fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);   
    }   
       
    /* Search for BGR values 0..255 (after net is unbiased) and return colour index  
       ---------------------------------------------------------------------------- */   
    public int map(int b, int g, int r) {   
   
        int i, j, dist, a, bestd;   
        int[] p;   
        int best;   
   
        bestd = 1000; /* biggest possible dist is 256*3 */   
        best = -1;   
        i = netindex[g]; /* index on g */   
        j = i - 1; /* start at netindex[g] and work outwards */   
   
        while ((i < netsize) || (j >= 0)) {   
            if (i < netsize) {   
                p = network[i];   
                dist = p[1] - g; /* inx key */   
                if (dist >= bestd)   
                    i = netsize; /* stop iter */   
                else {   
                    i++;   
                    if (dist < 0)   
                        dist = -dist;   
                    a = p[0] - b;   
                    if (a < 0)   
                        a = -a;   
                    dist += a;   
                    if (dist < bestd) {   
                        a = p[2] - r;   
                        if (a < 0)   
                            a = -a;   
                        dist += a;   
                        if (dist < bestd) {   
                            bestd = dist;   
                            best = p[3];   
                        }   
                    }   
                }   
            }   
            if (j >= 0) {   
                p = network[j];   
                dist = g - p[1]; /* inx key - reverse dif */   
                if (dist >= bestd)   
                    j = -1; /* stop iter */   
                else {   
                    j--;   
                    if (dist < 0)   
                        dist = -dist;   
                    a = p[0] - b;   
                    if (a < 0)   
                        a = -a;   
                    dist += a;   
                    if (dist < bestd) {   
                        a = p[2] - r;   
                        if (a < 0)   
                            a = -a;   
                        dist += a;   
                        if (dist < bestd) {   
                            bestd = dist;   
                            best = p[3];   
                        }   
                    }   
                }   
            }   
        }   
        return (best);   
    }   
    public byte[] process() {   
        learn();   
        unbiasnet();   
        inxbuild();   
        return colorMap();   
    }   
       
    /* Unbias network to give byte values 0..255 and record position i to prepare for sort  
       ----------------------------------------------------------------------------------- */   
    public void unbiasnet() {   
   
        int i, j;   
   
        for (i = 0; i < netsize; i++) {   
            network[i][0] >>= netbiasshift;   
            network[i][1] >>= netbiasshift;   
            network[i][2] >>= netbiasshift;   
            network[i][3] = i; /* record colour no */   
        }   
    }   
       
    /* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]  
       --------------------------------------------------------------------------------- */   
    protected void alterneigh(int rad, int i, int b, int g, int r) {   
   
        int j, k, lo, hi, a, m;   
        int[] p;   
   
        lo = i - rad;   
        if (lo < -1)   
            lo = -1;   
        hi = i + rad;   
        if (hi > netsize)   
            hi = netsize;   
   
        j = i + 1;   
        k = i - 1;   
        m = 1;   
        while ((j < hi) || (k > lo)) {   
            a = radpower[m++];   
            if (j < hi) {   
                p = network[j++];   
                try {   
                    p[0] -= (a * (p[0] - b)) / alpharadbias;   
                    p[1] -= (a * (p[1] - g)) / alpharadbias;   
                    p[2] -= (a * (p[2] - r)) / alpharadbias;   
                } catch (Exception e) {   
                } // prevents 1.3 miscompilation   
            }   
            if (k > lo) {   
                p = network[k--];   
                try {   
                    p[0] -= (a * (p[0] - b)) / alpharadbias;   
                    p[1] -= (a * (p[1] - g)) / alpharadbias;   
                    p[2] -= (a * (p[2] - r)) / alpharadbias;   
                } catch (Exception e) {   
                }   
            }   
        }   
    }   
       
    /* Move neuron i towards biased (b,g,r) by factor alpha  
       ---------------------------------------------------- */   
    protected void altersingle(int alpha, int i, int b, int g, int r) {   
   
        /* alter hit neuron */   
        int[] n = network[i];   
        n[0] -= (alpha * (n[0] - b)) / initalpha;   
        n[1] -= (alpha * (n[1] - g)) / initalpha;   
        n[2] -= (alpha * (n[2] - r)) / initalpha;   
    }   
       
    /* Search for biased BGR values  
       ---------------------------- */   
    protected int contest(int b, int g, int r) {   
   
        /* finds closest neuron (min dist) and updates freq */   
        /* finds best neuron (min dist-bias) and returns position */   
        /* for frequently chosen neurons, freq[i] is high and bias[i] is negative */   
        /* bias[i] = gamma*((1/netsize)-freq[i]) */   
   
        int i, dist, a, biasdist, betafreq;   
        int bestpos, bestbiaspos, bestd, bestbiasd;   
        int[] n;   
   
        bestd = ~(((int) 1) << 31);   
        bestbiasd = bestd;   
        bestpos = -1;   
        bestbiaspos = bestpos;   
   
        for (i = 0; i < netsize; i++) {   
            n = network[i];   
            dist = n[0] - b;   
            if (dist < 0)   
                dist = -dist;   
            a = n[1] - g;   
            if (a < 0)   
                a = -a;   
            dist += a;   
            a = n[2] - r;   
            if (a < 0)   
                a = -a;   
            dist += a;   
            if (dist < bestd) {   
                bestd = dist;   
                bestpos = i;   
            }   
            biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));   
            if (biasdist < bestbiasd) {   
                bestbiasd = biasdist;   
                bestbiaspos = i;   
            }   
            betafreq = (freq[i] >> betashift);   
            freq[i] -= betafreq;   
            bias[i] += (betafreq << gammashift);   
        }   
        freq[bestpos] += beta;   
        bias[bestpos] -= betagamma;   
        return (bestbiaspos);   
    }   
}   
